In their taxonomy, [1]} additionally propose a third group of algorithms, which alleviate dynamic programming to compute a disparity map. Algorithms in this group usually state an explicit smoothness assumption and approximate a global optimization scheme, which is why this group can be considered as a subgroup of the global methods. The most prominent algorithm, especially for real-time embedded processing, is the so-called *SGM algorithm [2]}, [3]}. In this, the optimization scheme is formulated as a *MRF and the minimization of the energy function with its explicit smoothness assumption is approximated by aggregating the matching costs along a number of concentric paths for each pixel \(\mathrm {p}\)  within the image domain:
\(  \begin{aligned} L_\mathrm {r}(\mathrm {p}, d) = \mathcal {S}(\mathrm {p}, d) &+ \min \limits _{d^{\prime }\in \Gamma }\left(L_\mathrm {r}(\mathrm {p-r}, d^{\prime })+\mathcal {V}(d,d^{\prime })\right),\ \text{with} \\ \mathcal {V}(d,d^{\prime }) &= {\left\lbrace \begin{array}{ll} 0 ,\ \text{if}\ d = d^{\prime } \\ P_1 ,\ \text{if}\ \left|d-d^{\prime }\right| = 1 \\ P_2 ,\ \text{if}\ \left|d-d^{\prime }\right| > 1. \end{array}\right.} \end{aligned} \) 
